window_MI = {}
i = self._tau_max
while i <= lx - self._window :
curr_coef_lag = []
for k in lag_array :
if k <= 0 : # For negative tau
curr_x = x[i : i + self._window].values
curr_y = y[i + k : i + k + self._window].values
else : # For positive tau
curr_x = x[i - k : i - k + self._window].values
curr_y = y[i : i + self._window].values
'Compute MI for current segments'
curr_MI = mi.compute([pd.DataFrame(curr_x), pd.DataFrame(curr_y)])
curr_coef_lag.append(curr_MI['MI'])
window_MI.update({float(i) : curr_coef_lag})
' go to the next window '
i += self._win_inc
window_MI['Lag'] = [float(x) for x in lag_array]
self.res = window_MI
self.plot()
return window_MI
window_MI = {}
i = self._tau_max
while i <= lx - self._window:
curr_coef_lag = []
fixed_x = pd.DataFrame(x[i:i + self._window].values)
fixed_y = pd.DataFrame(y[i:i + self._window].values)
# For negative tau
for k in lag_array[lag_array <= 0]:
curr_y = y[i + k:i + k + self._window].values
'Compute MI for current segments'
curr_MI = mi.compute(fixed_x, pd.DataFrame(curr_y))
curr_coef_lag.append(curr_MI['MI'])
# For positive tau
for k in lag_array[lag_array > 0]:
curr_x = x[i - k:i - k + self._window].values
'Compute MI for current segments'
curr_MI = mi.compute(pd.DataFrame(curr_x), fixed_y)
curr_coef_lag.append(curr_MI['MI'])
window_MI.update({float(i): curr_coef_lag})
' go to the next window '
i = self._tau_max
while i <= lx - self._window:
curr_coef_lag = []
for k in lag_array:
if k <= 0: # For negative tau
curr_x = x[i:i + self._window].values
curr_y = y[i + k:i + k + self._window].values
else: # For positive tau
curr_x = x[i - k:i - k + self._window].values
curr_y = y[i:i + self._window].values
'Compute MI for current segments'
curr_MI = mi.compute(
[pd.DataFrame(curr_x),
pd.DataFrame(curr_y)])
curr_coef_lag.append(curr_MI['MI'])
window_MI.update({float(i): curr_coef_lag})
' go to the next window '
i += self._win_inc
window_MI['Lag'] = [float(x) for x in lag_array]
self.res = window_MI
self.plot()
window_MI = {}
i = self._tau_max
while i <= lx - self._window:
curr_coef_lag = []
fixed_x = pd.DataFrame(x[i: i + self._window].values)
fixed_y = pd.DataFrame(y[i: i + self._window].values)
# For negative tau
for k in lag_array[lag_array <= 0]:
curr_y = y[i + k: i + k + self._window].values
'Compute MI for current segments'
curr_MI = mi.compute(fixed_x, pd.DataFrame(curr_y))
curr_coef_lag.append(curr_MI['MI'])
# For positive tau
for k in lag_array[lag_array > 0]:
curr_x = x[i - k: i - k + self._window].values
'Compute MI for current segments'
curr_MI = mi.compute(pd.DataFrame(curr_x), fixed_y)
curr_coef_lag.append(curr_MI['MI'])
window_MI.update({float(i) : curr_coef_lag})
' go to the next window '
